Wildlife and Habitat Conservation Scientist

Wildlife and Habitat Conservation Scientist (AI Training)

About The Role

Your field knowledge of wildlife biology and habitat conservation is more valuable than ever — and not just in the field. We're looking for conservation scientists to help train and evaluate AI systems that reason about biodiversity, ecological health, and conservation strategy.

This is a fully remote, flexible contract role where your scientific expertise directly shapes how AI understands the natural world. No prior AI experience required.

• Organization: Alignerr (Powered by Labelbox)
• Type: Hourly / Task-based Contract
• Location: Remote
• Commitment: 10–40 hours/week

What You'll Do

• Review AI-generated wildlife and habitat conservation scenarios for scientific accuracy
• Assess the quality of ecological reasoning and proposed conservation strategies
• Identify unrealistic assumptions, flawed methodologies, or misapplied conservation concepts
• Provide clear, structured feedback that improves the ecological validity of AI outputs
• Work independently and asynchronously — on your schedule, at your pace

Who You Are

• 3+ years of experience in wildlife biology, ecology, habitat conservation, or a closely related field
• Strong working knowledge of biodiversity principles and ecosystem management
• Able to critically evaluate applied ecological reasoning presented in written form
• Comfortable reading and reviewing structured scientific content
• Detail-oriented, self-motivated, and reliable when working independently

Nice to Have

• Graduate degree in Ecology, Wildlife Biology, Conservation Science, or a related discipline
• Hands-on field research or conservation program experience
• Familiarity with AI systems, content evaluation, or scientific annotation workflows

Why Join Us

• Meaningful impact — your expertise helps AI reason responsibly about conservation and the natural world
• Fully remote and flexible — work from anywhere, on a schedule that fits your life
• Cutting-edge work — gain direct exposure to how large language models are trained and evaluated
• Autonomy — task-based structure means you're in control of your workflow
• Global collaboration — connect with a diverse, expert community of scientists and researchers
• Ongoing opportunity — strong contributors are considered for contract extension and future projects